Why does Q-learning converge under 100% exploration rate?
Q-learning is guaranteed to converge (in the tabular case) under some mild conditions, one of which is that in the limit we visit each state-action tuple infinitely many times. If your random random policy (i.e. 100% exploration) is guaranteeing this and the other conditions are met (which they probably are) then Q-learning will converge.
The reason that different state-action pairs have longer arrows, i.e. higher Q-values, is simply because the value of being in that state-action pair is higher. An example would be the arrow pointing down right above the trophy -- obviously this has the highest Q-value as the return is 1. For all other states it will be $\gamma^k$ for some $k$ -- to see this remember that a Q-value is defined as
$$Q(s, a) = \mathbb{E}_\pi \left[\sum_{j=0}^\infty \gamma^j R_{t+j+1} |S_t = s, A_t = a \right]\;;$$
so for any state-action pair that is not the block above the trophy with the down arrow $\sum_{j=0}^\infty \gamma^j R_{t+j+1}$ will be a sum of $0$'s plus $\gamma^T$ where $T$ is the time that you finally reach the trophy (assuming you give a reward of 1 for reaching the trophy).